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Interest in generative models has grown tremendously in the past decade.However, their training performance can be adversely affected by contamination,where outliers are encoded in the representation of the model. This results inthe generation of noisy data. In this paper, we introduce weighted conjugatefeature duality in the framework of Restricted Kernel Machines (RKMs). The RKMformulation allows for an easy integration of methods from classical robuststatistics. This formulation is used to fine-tune the latent space ofgenerative RKMs using a weighting function based on the Minimum CovarianceDeterminant, which is a highly robust estimator of multivariate location andscatter. Experiments show that the weighted RKM is capable of generating cleanimages when contamination is present in the training data. We further show thatthe robust method also preserves uncorrelated feature learning throughqualitative and quantitative experiments on standard datasets.

Robust Generative Restricted Kernel Machines using Weighted Conjugate Feature Duality